CN117194471A - Data blood edge analysis method, device, medium, electronic equipment and product - Google Patents
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Abstract
The application discloses a data blood-edge analysis method, a device, a medium, electronic equipment and a product. The method comprises the following steps: acquiring a tail end operation node of a target scene, and determining an operation link of the target scene based on the tail end operation node; extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information; screening the historical execution sentences based on the execution host information and the operation time information of each operation node to obtain target execution sentences; and constructing a data blood margin analysis result of the target scene based on the target execution statement. The data blood edge analysis aiming at the target scene is realized, and the accuracy of the data blood edge analysis result of the target scene is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a medium, an electronic device, and a product for data blood edge analysis.
Background
Along with the development and application of big data technology, the demand of blood relationship analysis of data appears in the big data treatment field.
Data blood-edge analysis is obtained by determining the upstream and downstream relationships between data during data processing. In the process of running a system or a mechanism, different application scenarios exist, but in the current data blood edge analysis process, the data blood edge analysis result of a certain scenario cannot be determined in a targeted manner based on a large amount of data processing in the running process of the system or the mechanism (for example, the system or the mechanism can be included).
Disclosure of Invention
The application provides a data blood edge analysis method, a device, a medium, electronic equipment and a product, which can obtain the data blood edge analysis result of a target scene, and have high accuracy and are not influenced by non-target scene data.
According to an aspect of the present application, there is provided a data blood-lineage analysis method including:
acquiring a tail end operation node of a target scene, and determining an operation link of the target scene based on the tail end operation node;
extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information;
screening the historical execution sentences based on the execution host information and the operation time information of each operation node to obtain target execution sentences;
and constructing a data blood margin analysis result of the target scene based on the target execution statement.
Optionally, the target scenario includes a supervision scenario, and an end job node of the supervision scenario is a job node that sends supervision data to the supervision device.
Optionally, the determining, based on the end job node, a job link of the target scenario includes:
and sequentially determining multi-stage upstream operation nodes of the tail end operation nodes based on the dependency relationship among the operation nodes, wherein the tail end operation nodes and the multi-stage upstream operation nodes form an operation link.
Optionally, the job time information includes a job start time and a job end time;
the filtering the historical execution statement based on the execution host information and the job time information of each job node to obtain a target execution statement includes: a historical execution statement that matches execution host information of the job node and whose execution timestamp is within the job start time and the job end time is determined as a target execution statement.
Optionally, the target execution statement includes source data and target data;
the constructing the data blood edge analysis result of the target scene based on the target execution statement comprises the following steps: for each target execution statement, extracting source data and target data in the target execution statement, and establishing a data blood relationship between the source data and the target data; and forming a data blood edge analysis result based on the data blood edge relation corresponding to the target execution sentences.
Optionally, the source data and the target data respectively include one or more of the following: system data, table data, field data; correspondingly, the data blood-edge relationship comprises a system-level blood-edge relationship, a table-level blood-edge relationship and a field-level blood-edge relationship.
According to an aspect of the present application, there is provided a data blood-lineage analysis device including:
the operation link determining module is used for acquiring a tail end operation node of a target scene and determining an operation link of the target scene based on the tail end operation node;
the job information extraction module is used for extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information;
the statement screening module is used for screening the historical execution statements based on the execution host information and the operation time information of each operation node to obtain target execution statements;
and the data blood edge construction module is used for constructing a data blood edge analysis result of the target scene based on the target execution statement.
Optionally, the job link determination module is configured to: and sequentially determining multi-stage upstream operation nodes of the tail end operation nodes based on the dependency relationship among the operation nodes, wherein the tail end operation nodes and the multi-stage upstream operation nodes form an operation link.
Optionally, the job time information includes a job start time and a job end time;
the statement screening module is used for: a historical execution statement that matches execution host information of the job node and whose execution timestamp is within the job start time and the job end time is determined as a target execution statement.
Optionally, the target execution statement includes source data and target data;
the data blood-edge construction module is used for extracting source data and target data in each target execution statement and establishing a data blood-edge relation between the source data and the target data; and forming a data blood edge analysis result based on the data blood edge relation corresponding to the target execution sentences.
According to another aspect of the present application, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data blood-lineage analysis method according to any of the embodiments of the present application.
According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a data blood-edge analysis method according to any one of the embodiments of the present application.
According to another aspect of the present application, there is provided a computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the data blood-edge analysis method according to any of the embodiments of the present application.
According to the technical scheme, the operation link of the target scene is determined, the target execution statement of the operation link is matched, the history execution statement outside the target scene is removed, and the interference execution statement is reduced. The data blood edge analysis result of the target scene is constructed through the target execution statement, so that the data blood edge analysis of the target scene is realized, and the accuracy of the data blood edge analysis result of the target scene is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for data blood-lineage analysis according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a job link provided by an embodiment of the present application;
FIG. 3 is a flow chart of a method for data blood-lineage analysis according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a data blood-edge analysis device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that, in the description and claims of the present application and the above figures, the terms "first feature data", "second feature data", and the like are used to distinguish similar objects, and are not necessarily used to describe a specific order or precedence. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical scheme of the application obtains, stores and/or processes the data, and accords with the relevant regulations of national laws and regulations.
Example 1
Fig. 1 is a flowchart of a data blood edge analysis method according to an embodiment of the present application, where the method may be performed by a data blood edge analysis device, which may be implemented in hardware and/or software, and the data blood edge analysis device may be configured in an electronic device according to an embodiment of the present application, where a suitable post is recommended to a new staff member. As shown in fig. 1, the method includes:
s110, acquiring a tail end operation node of a target scene, and determining an operation link of the target scene based on the tail end operation node.
S120, extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information.
S130, screening the historical execution sentences based on the execution host information and the job time information of each job node to obtain target execution sentences.
S140, constructing a data blood edge analysis result of the target scene based on the target execution statement.
In the operation process of the service system, a plurality of application scenes can be included, and different service systems or different mechanisms to which the service systems belong can be included. Taking a financial institution as an example, the business scenario of the business system in the operation process includes, but is not limited to, a transaction scenario, a supervision scenario, and the like. The target scene is any one of the application scenes, and can be determined according to the analysis requirement of the data blood edges, and the target scene can be preset.
Each application scene is required to be executed through an operation link, the operation link comprises a plurality of operation nodes, the combination mode of the operation nodes in the operation link is variable, and the operation nodes in different operation links can be overlapped. It will be appreciated that there are dependencies between adjacent job nodes in the job link, and other job nodes in the job link are derived in reverse by determining the end job node of the application scenario to form the job link.
The result data obtained by different application scenes are different, each piece of the result data corresponds to one application scene, and the processing process for obtaining the result data can determine the execution process of the application scene. Taking the target scenario as an example of the supervision scenario, the result data is supervision data, and the supervision data may be in the form of report forms, which are not limited herein, and each supervision data corresponds to a supervision scenario. Optionally, in the event that regulatory data is detected, triggering the performance of data blood-edge analysis of the regulatory scenario. Correspondingly, for other application scenes, the data blood-edge analysis of the application scene can be triggered under the condition that the result data of the application scene is detected.
The end job node of the target scene is a job node outputting the result data, taking the supervision scene as an example, and the end job node of the supervision scene is a job node sending the supervision data to the supervision equipment. Alternatively, the job node may be an etl (Extract, transform and Load, extract, convert and load) job node.
Determining a working link of a target scene based on an end working node, specifically, sequentially determining a multi-stage upstream working node of the end working node based on the dependency relationship among the working nodes, wherein the dependency relationship among the working nodes is preconfigured, and the dependency relationship among different working nodes can be determined by reading a dependency relationship configuration file. For the upstream job node which depends on the end job node and the upstream job node which depends on the upstream job node are determined through the dependency relationship, and the like until any upstream job node does not exist.
And forming an operation link between the end operation node and the multi-stage upstream operation node, wherein the end operation node and a plurality of operation nodes in the multi-stage upstream operation node can be ordered based on a dependency relationship or an upstream-downstream relationship, or the operation nodes are connected in series based on the dependency relationship or the upstream-downstream relationship.
Referring to fig. 2, fig. 2 is a schematic diagram of an operation link according to an embodiment of the present application. The end job node of the target scene may be the job node 4 and the job node 8, respectively determine the multi-stage upstream job node on which the job node 4 depends and the multi-stage upstream job node on which the job node 8 depends, and obtain the job links corresponding to the job node 4 and the job node 8 respectively.
The data processed by the operation nodes in the operation link in the execution process is determined to be the data in the target scene, and the data blood-edge analysis is performed through the data in the target scene, so that the interference of the data outside the target scene is avoided, and the accuracy of the data blood-edge analysis is improved. In the operation process of each operation node, a history execution statement is generated, and the history execution statement can be used as log information, so that the operation process of the operation node can be conveniently inquired. Wherein the historical execution statement may be an sql (Structured Query Language ) statement.
The history execution statement may be used as log information, and record relevant information of data processing in the operation process of the operation node. By determining the target execution statement corresponding to the target scene in all the history execution statements of the business system or the organization, the interference of other history execution statements on the data blood edge analysis can be reduced.
Wherein each job node can configure job information including, but not limited to, the job node name, job trigger condition, condition name, job calling mode, job status, job time information including job start time and job end time, and execution subject information. After determining the worker links, the worker links may be verified based on the worker time of each worker node in the worker links. Wherein the working time of the upstream working node in the working link is earlier than the working time of the downstream working node.
And extracting the job time information and the execution subject information in the job information, and screening the historical execution sentences. Wherein, the history execution statement comprises execution host information and an execution time stamp. And matching the job time information and the execution subject information in the job information with the execution host information and the execution time stamp in the historical execution statement, and determining the historical execution statement matched with the job node as a target execution statement.
Optionally, filtering the historical execution statement based on the execution host information and the job time information of each job node to obtain a target execution statement, including: a historical execution statement that matches execution host information of the job node and whose execution timestamp is within the job start time and the job end time is determined as a target execution statement.
Matching the execution main body information of the historical execution statement with the execution host information of each job node, and under the condition that the matching with any job node is successful, matching the execution time stamp of the historical execution statement with the job information of the job node, determining whether the execution time stamp of the historical execution statement is positioned between the job starting time and the job ending time of the job node, if so, determining the historical execution statement as a target execution statement in a target scene, wherein the target execution statements matched with a plurality of job nodes can form a target execution statement set of the target scene.
And carrying out data blood edge analysis through a target execution statement set of the target scene to obtain a data blood edge analysis result of the target scene. Each target execution statement includes source data and target data, wherein the source data is data before being processed in a certain processing process, the target data is data processed by the processing process, and the data A is data B obtained by the processing process, and the data A is source data and the data B is target data. And determining that the source data is upstream data of the target data, and the target data is downstream data of the source data. The target execution statement comprises a data identifier of the source data and a data identifier of the target data, and the data blood-edge relationship corresponding to the target execution statement can be determined through the data identifier of the source data and the data identifier of the target data.
Correspondingly, constructing a data blood edge analysis result of the target scene based on the target execution statement, including: for each target execution statement, extracting source data and target data in the target execution statement, and establishing a data blood relationship between the source data and the target data; and forming a data blood edge analysis result based on the data blood edge relation corresponding to the target execution sentences.
Based on the data identification of the source data and the data identification of the target data, respectively creating data nodes of the source data and the target data, and establishing blood-edge association between the data nodes of the source data and the data nodes of the target data, for example, the data nodes of the source data and the data nodes of the target data can be connected in a manner of arrows or connecting lines so as to represent the data blood-edge relationship between the source data and the target data. And integrating the data blood edge relations corresponding to the target execution sentences corresponding to the target scene to obtain a data blood edge analysis result of the target scene. For example, the same data nodes in different data blood-edge relations are combined to realize the combination of different data blood-edge relations, the data A and the data B have the data blood-edge relations, the data B and the data C have the data blood-edge relations in sequence, the data nodes of the data B in the two data blood-edge relations are combined to obtain the data blood-edge relations of the data A, the data B and the data C, and the data blood-edge analysis result is obtained by analogy.
On the basis of the above embodiment, the source data and the target data respectively include one or more of the following: system data, table data, field data. Wherein the data being processed may belong to different physical systems, which may be the home of the data, e.g. a business system may be a physical system, e.g. an application set may be a physical system, etc. The system data is used for characterizing the system to which the data belongs, any system can comprise a plurality of table data, the table data is data in a table form, and the table data can comprise field data. Illustratively, the target execution statement is parsed to obtain field data M of table data 1 in system 1 as the source data and field data N of table data B in system 2 as the target data.
Correspondingly, the data blood-edge relationship comprises a system-level blood-edge relationship, a table-level blood-edge relationship and a field-level blood-edge relationship. The system-level blood-edge relationships are created based on the system data, the table-level blood-edge relationships are created based on the table data, and the field-level blood-edge relationships are created based on the field data, the creation process of which is not described here. Correspondingly, the data blood edge analysis result comprises a system-level blood edge relation, a table-level blood edge relation and a field-level blood edge relation.
On the basis of the above embodiment, the embodiment of the application also provides an embodiment of a data blood-edge analysis method in a supervision scene, and referring to fig. 3, fig. 3 is a flowchart of the data blood-edge analysis method provided by the embodiment of the application. Creating a job link of a supervision scene through an end execution node in the supervision scene, acquiring running water of a plurality of job nodes in the job link of the supervision scene, wherein the running water comprises job information of the plurality of job nodes in the job link, the job information comprises execution host information, job starting time and job ending time, matching is carried out in historical execution sentences through the execution host information, the job starting time and the job ending time, a set of target execution sentences in the supervision scene is obtained, and a data blood-edge analysis result of the supervision scene is created through the target execution sentences.
According to the technical scheme provided by the embodiment of the application, the historical execution sentences outside the target scene are removed by determining the operation link of the target scene and matching the operation link with the target execution sentences of the operation link, so that the interference execution sentences are reduced. The data blood edge analysis result of the target scene is constructed through the target execution statement, so that the data blood edge analysis of the target scene is realized, and the accuracy of the data blood edge analysis result of the target scene is improved.
Example two
Fig. 4 is a schematic structural diagram of a data blood edge analysis device according to a second embodiment of the present application.
As shown in fig. 4, the apparatus includes:
a job link determination module 210, configured to obtain an end job node of a target scene, and determine a job link of the target scene based on the end job node;
a job information extraction module 220, configured to extract job information of a plurality of job nodes in the job link, where the job information includes execution host information and job time information;
the statement screening module 230 is configured to screen the historical execution statement based on the execution host information and the job time information of each job node, so as to obtain a target execution statement;
the data blood edge construction module 240 is configured to construct a data blood edge analysis result of the target scene based on the target execution statement.
On the basis of the above embodiment, optionally, the target scenario includes a supervision scenario, and an end job node of the supervision scenario is a job node that sends supervision data to the supervision device.
On the basis of the above embodiment, optionally, the job link determination module 110 is configured to:
and sequentially determining multi-stage upstream operation nodes of the tail end operation nodes based on the dependency relationship among the operation nodes, wherein the tail end operation nodes and the multi-stage upstream operation nodes form an operation link.
Optionally, the job time information includes a job start time and a job end time;
statement screening module 230 is configured to: a historical execution statement that matches execution host information of the job node and whose execution timestamp is within the job start time and the job end time is determined as a target execution statement.
On the basis of the above embodiment, optionally, the target execution statement includes source data and target data;
the data blood-edge construction module 210 is configured to extract, for each target execution statement, source data and target data in the target execution statement, and establish a data blood-edge relationship between the source data and the target data; and forming a data blood edge analysis result based on the data blood edge relation corresponding to the target execution sentences.
On the basis of the above embodiment, optionally, the source data and the target data respectively include one or more of the following: system data, table data, field data;
correspondingly, the data blood-edge relationship comprises a system-level blood-edge relationship, a table-level blood-edge relationship and a field-level blood-edge relationship.
The data blood edge analysis device provided by the embodiment of the application can execute the data blood edge analysis method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
Fig. 5 is a schematic structural diagram of an electronic device according to a third embodiment of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the data blood-lineage analysis method.
In some embodiments, the data lineage analysis method can be implemented as a computer program, which is tangibly embodied in a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the data blood-edge analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data blood-lineage analysis method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
Example IV
The fourth embodiment of the present application also provides a computer readable storage medium storing computer instructions for causing a processor to perform a data blood-edge analysis method, the method comprising:
acquiring a tail end operation node of a target scene, and determining an operation link of the target scene based on the tail end operation node; extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information; screening the historical execution sentences based on the execution host information and the operation time information of each operation node to obtain target execution sentences; and constructing a data blood margin analysis result of the target scene based on the target execution statement.
In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present application are achieved, and the present application is not limited herein.
Example five
A fifth embodiment of the application also provides a computer program product comprising a computer program which, when executed by a processor, implements a data blood-edge analysis method according to any embodiment of the application.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (15)
1. A method of data blood-edge analysis, comprising:
acquiring a tail end operation node of a target scene, and determining an operation link of the target scene based on the tail end operation node;
extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information;
screening the historical execution sentences based on the execution host information and the operation time information of each operation node to obtain target execution sentences;
and constructing a data blood margin analysis result of the target scene based on the target execution statement.
2. The method of claim 1, wherein the target scenario comprises a supervision scenario, an end job node of the supervision scenario being a job node that sends supervision data to a supervision device.
3. The method of claim 1, wherein the determining a job link for the target scenario based on the end job node comprises:
and sequentially determining multi-stage upstream operation nodes of the tail end operation nodes based on the dependency relationship among the operation nodes, wherein the tail end operation nodes and the multi-stage upstream operation nodes form an operation link.
4. The method of claim 1, wherein the job time information includes a job start time and a job end time;
the filtering the historical execution statement based on the execution host information and the job time information of each job node to obtain a target execution statement includes:
a historical execution statement that matches execution host information of the job node and whose execution timestamp is within the job start time and the job end time is determined as a target execution statement.
5. The method of claim 1, wherein the target execution statement includes source data and target data;
the constructing the data blood edge analysis result of the target scene based on the target execution statement comprises the following steps:
for each target execution statement, extracting source data and target data in the target execution statement, and establishing a data blood relationship between the source data and the target data;
and forming a data blood edge analysis result based on the data blood edge relation corresponding to the target execution sentences.
6. The method of claim 5, wherein the source data and the target data each comprise one or more of: system data, table data, field data;
correspondingly, the data blood-edge relationship comprises a system-level blood-edge relationship, a table-level blood-edge relationship and a field-level blood-edge relationship.
7. A data blood edge analysis device, comprising:
the operation link determining module is used for acquiring a tail end operation node of a target scene and determining an operation link of the target scene based on the tail end operation node;
the job information extraction module is used for extracting job information of a plurality of job nodes in the job link, wherein the job information comprises execution host information and job time information;
the statement screening module is used for screening the historical execution statements based on the execution host information and the operation time information of each operation node to obtain target execution statements;
and the data blood edge construction module is used for constructing a data blood edge analysis result of the target scene based on the target execution statement.
8. The apparatus of claim 7, wherein the target scenario comprises a supervision scenario, an end job node of the supervision scenario being a job node that sends supervision data to a supervision device.
9. The apparatus of claim 7, wherein the job link determination module is to:
and sequentially determining multi-stage upstream operation nodes of the tail end operation nodes based on the dependency relationship among the operation nodes, wherein the tail end operation nodes and the multi-stage upstream operation nodes form an operation link.
10. The apparatus of claim 7, wherein the job time information includes a job start time and a job end time;
the statement screening module is used for: a historical execution statement that matches execution host information of the job node and whose execution timestamp is within the job start time and the job end time is determined as a target execution statement.
11. The apparatus of claim 7, wherein the target execution statement includes source data and target data;
the data blood-edge construction module is used for extracting source data and target data in each target execution statement and establishing a data blood-edge relation between the source data and the target data;
and forming a data blood edge analysis result based on the data blood edge relation corresponding to the target execution sentences.
12. The apparatus of claim 11, wherein the source data and the target data each comprise one or more of: system data, table data, field data;
correspondingly, the data blood-edge relationship comprises a system-level blood-edge relationship, a table-level blood-edge relationship and a field-level blood-edge relationship.
13. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data blood-edge analysis method of any one of claims 1-6.
14. A computer readable storage medium storing computer instructions for causing a processor to perform the data lineage analysis method according to any one of claims 1-6.
15. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the data blood-edge analysis method according to any one of claims 1-6.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311111955.XA CN117194471A (en) | 2023-08-30 | 2023-08-30 | Data blood edge analysis method, device, medium, electronic equipment and product |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311111955.XA CN117194471A (en) | 2023-08-30 | 2023-08-30 | Data blood edge analysis method, device, medium, electronic equipment and product |
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| CN117194471A true CN117194471A (en) | 2023-12-08 |
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| CN202311111955.XA Pending CN117194471A (en) | 2023-08-30 | 2023-08-30 | Data blood edge analysis method, device, medium, electronic equipment and product |
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